Small-sized portable mobile devices, including smartphones and tablet computers, are becoming extremely prevailing. These pocket-sized gadgets have a set of embedded sensors and can provide abundant sensing data about the environment and human society, thus offering great opportunities to carry out crowdsensing. One primary objective of this project is to develop a mobile crowdsensing framework with fair pricing and task allocation. A key challenge is that different parties involved in mobile crowdsensing, including mobile users, task owners, and the platform, have conflicting interests: 1) mobile users aim to maximize the profit for performing sensing tasks; 2) task owners strive to get their sensing tasks performed with high quality of sensing, at a cost as small as possible; and 3) the platform would desire social welfare maximization. Based on recent advances in Exchange Economy theory, this project will tackle this challenge to strike a right balance and enable them to work in concert. This project serves as an excellent example for exploring innovative research on the interplay among engineering, economics and operation research. It will spur a new line of thinking for large-scale mobile sensing in applications including smart health and smart city, benefiting the society at large. Another major task of this project is to integrate research into educational activities.
Appealing to Exchange Economy theory, this project employs the notion of "Walrasian Equilibrium" as the overall metric, at which there exists a price vector for mobile users and an allocation for task owners, such that the allocation is Pareto optimal and the market gets cleared (i.e., all sensing tasks are performed). Under the common theme of joint pricing and task scheduling with constraints, this project is centered around devising algorithms that can achieve a Walrasian Equilibrium, for both cases where sensing tasks are either divisible or indivisible. Thrust I studies joint pricing and task allocation for crowdsensing with divisible sensing tasks, via a strategic bargaining approach. The existence of a Walrasian Equilibrium will be investigated first, together with a centralized scheme used as a benchmark. Then, based on multi-lateral bargaining theory, decentralized algorithms will be devised where mobile users and task owners negotiate with each other to determine the pricing and allocation, and the convergence of the bargaining game output to a Walrasian Equilibrium will be investigated thoroughly. Thrust II will be devoted to joint pricing and allocation for crowdsensing with indivisible sensing tasks. One challenge in this more sophisticated setting is that there may not exist a Walrasian Equilibrium. In light of this, the notion of Combinatorial Walrasian Equilibrium (a relaxation of Walrasian Equilibrium) will be applied to characterize an "optimal state." Since this relaxation may give rise to some inefficiency issues, the Tatonnement based approach will be taken to quantify the corresponding performance, in terms of the ratios to approximate the optimal social welfare and individual revenue. Further, decentralized solutions will be developed to achieve a Combinatorial Walrasian Equilibrium.
